Artificial intelligence. It's been with us for longer than we
think. Bank of America's Brian Moynihan says
they've been using it for at least five years.
We have this thing called Erica, which is a virtual artificial intelligence,
virtual assistant, natural language processing, predictive technology that
we've been running for five years. And people like JP Morgan's Jamie
Diamond insist that it is transformational.
A.I. is real.
This is not that's not crypto.
This is a technology which is staggering with A.I.
pioneers Samuel Altman telling Congress. We think it can be a printing press
moment. We have to work together to make it so.
But now there are those who are warning about some of the downsides with tech
veterans like Carly Fiorina agreeing with open eyes.
Samuel Altman, that there needs to be regulation.
Big tech has to say we alone are not in charge and we alone cannot control.
And a group of A.I. industry leaders this week wrote an open
letter warning that what they are building could lead to risk of
extinction. And to take a stern cell of the possible
impediments to general A.I. as well as the risks involved.
Welcome now, Sam Palmisano. He is now the chairman of the Center for
Global Enterprise. But of course, for many years he was the
chair and CEO of IBM. So, Sam, welcome back to Wall Street.
We've really great to have you here. Let's start, first of all, with exactly
how big this general could be. We've seen in video this this week
really explode in its market cap position.
Is it overhyped? Do you think?
Is it as big as people say? David?
Well, first of all, thank you for having me.
No, I think it's that big. This is transformational technology.
But it's early stage. But I mean, I always draw the analogy to
the Internet, because if you think back where it began exchanging technical
documents around the world in the 70s to what it's become today, 50 years later,
a lot of happened in innovation and development and regulation and guidance,
et cetera. All that has to happen with general A.I.
or large language models. But it is that transformation,
transformational because like the browser for the Internet made it kind of
user friendly for people to actually get in there and look up information or
search. This will do the same thing as far as a
ISE concern for people who use natural language to interface to it and get
responses that are very much like human being responses.
So if it is transformational, as you say, what are we going to need to have
it be that transformational? What is some of the key inputs?
Let's start, for example, with microchips.
That's what's driving in video to a large degree and maybe some other chip
manufacturer as well. What is the need there?
Well, basically, this is going to be it requires massive infrastructure.
And today you think about this hyperscale or is this the term or cloud
computing service providers like Amazon, Google and Microsoft, etc.?
Having said that, this is a huge demands on capacity and performance of that
capacity. Therefore, the chips and the advantage
that end video has these things, graphical processing units that are
called GP use those technologies themselves are much better performers
than the classic Intel chip, what's referred to as X 86 technology.
So fundamentally you need that level of performance to run this like these
natural language models because they're so large that data sets are so large and
require so many interconnections that you need the performance to be
acceptable to the end user. One of the things we hear about is
energy also. It didn't seem that long ago we were
talking about crypto mining and how what a big drain that was for energy.
China really cut back on some of the mining there in China.
What is the demand potentially in this transformational generative A.I.
or AM? What is the demand for energy?
Oh, it's the same as all these large cloud service data centers.
I mean, it's massive. I mean, you could bottle those as if the
nuclear reactors, not so much energy they consume.
Now, there's a lot that can be done. I mean, quite honestly, around the
utilization of this infrastructure to cut the energy costs, there's ways you
can use Jun cheap green energy to supplement the classical forms of
electricity in those sorts of things. But there are lots of techniques
associated with how to reduce that. But energy is going to be significant.
There's no doubt about it already is. I mean, it has been for years in these
large whether they were supercomputers in the old days or now they cloud
infrastructure provider as it's the same issue.
They require significant amounts of energy.
So, Sam, we've heard an awful lot about the wonderful things that generally I
could bring us and not least of which is increase productivity.
There are remarkable projections here of trillions of dollars of increased
production because of a general. Then we had a letter that came out this
week from a group of A.I. experts plunging over this warning about
the risks and even saying there's a risk of extinction if we're not careful.
Is that overblown? Well, I say let me tell you what the
risk are. I don't know if it's extinction, but the
risk are quite honestly. This technology makes you feel like
you're actually getting something. That's correct.
Right. But it's not it's not valid.
And what it's doing basically at the engineering designs, connecting large
scale words together. But there's no check into the source of
the words. There's no fact checking as to
conclusions these words reach. In some cases, it's a terrific essay
that it's prepared. Maybe it's a great book that it's
written or a wonderful poem. But at the end of the day, if you need
accuracy and you have to have accuracy, like in health care, if you were
depending upon your diagnostics, you would not use Chachi CBT mean you want
to have a doctor or some human of some kind that looked at the conclusion that
it reached, even though it probably read all the medical journals like we did in
Watson Health. You need some kind of a cognition
capability, which it does not have. I think people get confused by what it
does have because it's so it's so easy to use it.
So I mean, it's very, very attractive. There's no doubt about it.
But it needs to be enhanced with some of these capabilities that will address the
concerns that are in that article today about worrying about a.
Be like nuclear warfare in the future. I mean, if you think about applications
that could have a short term impact that you would like to have facts but don't
have any facts, take political propaganda as an example that are
political campaigns. I mean, you'd like to have some facts
and all that information, but we all know they don't exist.
And I'm not sure that proponents of all that information really want the facts
out there anyway. Sam, is that risk is that weakness of
actually truth checking? Is that inherent in large language
models? Is it inherent in general A.I.
or is it something with new iterations? Actually, it could take care of itself
so it could actually check itself. Would you what needs to be done is that
self checking, like you've said, that does not exist today?
I'd make the argument that the inventors of this technology that are raising all
these alarms knew about these alarms and they could have written some of these
checkers, these tracing of the information or transparency of the data
into the actual technology itself. Now, however, you know, I understand
that they raised the concerns. They are very, very valid concerns.
However, if you are the inventor or the guys coming up with this technology, you
knew this and you could have prepared for this, but now it has to be done.
I mean, it's great technology, it's transformational, but now it has to be
done and it will be done. And there's going to be lots of small,
innovative companies that are going to take advantage of that gap and add that
capability. What's the government to do?
It depends, you know. Right.
This is a great dilemma. And I take you back to the early days of
the Internet. I mean, in the early days of the
Internet, the government decided to take a light hand of guys to the Internet.
And the reason being that they would like to see the innovation
grow and accelerate before they actually put the, let's say, the regulations in
place. And they're still trying to do that
today. As you look at data privacy and those
kinds of issues, which are very, very valid issues.
And this is the same thing, I think, in the beginning.
How do you regulate something that you don't know what it is?
And we really don't know. We have concerns about what it could be,
but that's not what it is today. So my suggestion to the people that are
raising all these concern is they work with obviously the technologists, the
private sector, the academic universities, government regulations and
come up with guardrails, guardrails and not regulations.
Because once you put this in law or code, it never gets reversed.
And I don't think we know enough today to put that in a responsible way.
Put that in place. I know some of the founders of this
technology are calling for that. I understand that.
I understand why they're raising those concerns.
However, it's the actual details that matter in this case.
And you're trading off innovation because you could solve massive societal
problems with this technology. I mean, just think about where we have
inadequacies of consistency of quality health care as a system that doesn't
have consistency of quality. Suppose you make everybody an expert in
health care how much better we all would be education, how much better what we
all would be. So there's great benefit in the
technology. I just think that we ought to approach
it in a maybe a little more heavier hand than the Internet, but certainly not a
heavy handed regulation that perhaps is existing today in China.